Anomaly detection with a computing platform is important to monitoring and maintaining the health of the system. Anomaly detection can be used to alert developers and other system administrators to unexpected behaviors. However, building anomaly detection with the right granularity is challenging. If the anomaly detection is tuned or trained for normal day-to-day operation, seasonal spikes can result in false alarms. If the anomaly detection is tuned or trained for seasonal patterns, irregularities within smaller time frames may be missed. Thus, there is a need in the platform anomaly detection field to create a new and useful system and method for detecting platform anomalies through neural networks. This invention provides such a new and useful system and method.